o4-mini-deep-research vs Qwen2.5-VL 7B Instruct
o4-mini-deep-research comes out ahead, 49 to 40 on our weighted score.
- Our pick
OpenAIo4-mini-deep-research
49/100- ECI—
- Price—
- Context200K
Alibaba (Qwen)Qwen2.5-VL 7B Instruct
40/100- ECI—
- Price$0.35 / $1.05
- Context131K
Add a model
Make it a three-way comparison.
o4-mini-deep-research is our pick
o4-mini-deep-research is the better all-round choice, scoring 49/100 against Qwen2.5-VL 7B Instruct (40). It leads on inputs & features and context window. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceQwen2.5-VL 7B InstructQwen2.5-VL 7B Instruct $0.525 per 1M tokens (3:1 blend) · o4-mini-deep-research unpriced
- Longest contexto4-mini-deep-researcho4-mini-deep-research 200,000 · Qwen2.5-VL 7B Instruct 131,072 tokens
- Widest inputsSame inputso4-mini-deep-research: Text, Images · Qwen2.5-VL 7B Instruct: Text, Images
- Self-hostingQwen2.5-VL 7B InstructPublishes downloadable weights
| Measure | Weight | o4-mini-deep-research | Qwen2.5-VL 7B Instruct |
|---|---|---|---|
| Inputs & features | 60% | 60 | 50 |
| Context window | 40% | 32 | 24 |
| Overall | 100% | 49/100 | 40/100 |
Left out because at least one model lacks the data: capability and price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | o4-mini-deep-researchOpenAI | Qwen2.5-VL 7B InstructAlibaba (Qwen) |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | — | $0.35 |
| Output | — | $1.05 |
| Cached input | — | — |
| Blended (3:1) | — | $0.525 |
| Long-context rate | — | Same rate |
| Price source | — | Official Alibaba API |
| Limits | ||
| Context window | 200,000 tokens (best) | 131,072 tokens |
| Max output | 100,000 tokens (best) | 8,192 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | Yes |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yes | No |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | Proprietary | Open |
| API model ID | — | qwen2-5-vl-7b-instruct |
| API providers | — | 1 |
| Released | Jun 26, 2024 | Sep 2024 |
| Knowledge cutoff | May 2024 | Apr 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- o4-mini-deep-research—
- Qwen2.5-VL 7B Instruct$5.60
Which should you choose?
Which is better: o4-mini-deep-research or Qwen2.5-VL 7B Instruct?
o4-mini-deep-research is the better all-round choice, scoring 49/100 against Qwen2.5-VL 7B Instruct (40). It leads on inputs & features and context window. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, o4-mini-deep-research or Qwen2.5-VL 7B Instruct?
Qwen2.5-VL 7B Instruct is cheaper at $0.35 input / $1.05 output per million tokens (official Alibaba API price). . At a typical mix of three input tokens to one output token, that is $0.525 per million tokens for Qwen2.5-VL 7B Instruct versus . o4-mini-deep-research has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. o4-mini-deep-research has not been scored yet and Qwen2.5-VL 7B Instruct has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for o4-mini-deep-research and Qwen2.5-VL 7B Instruct yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Both support tool calling for agent workflows.
Which has the bigger context window?
o4-mini-deep-research has the largest context window at 200,000 tokens, against 131,072 for Qwen2.5-VL 7B Instruct. Maximum output per response: o4-mini-deep-research up to 100,000, Qwen2.5-VL 7B Instruct up to 8,192 tokens.
Which can read images, PDFs, audio or video?
o4-mini-deep-research accepts text and images; Qwen2.5-VL 7B Instruct accepts text and images. They handle the same number of input types.
Are any of these open source?
Qwen2.5-VL 7B Instruct publishes its weights and can be self-hosted; o4-mini-deep-research is proprietary.
Which is newer?
Qwen2.5-VL 7B Instruct is the newest, released Sep 2024. o4-mini-deep-research came out Jun 26, 2024. Knowledge cutoff: o4-mini-deep-research May 2024, Qwen2.5-VL 7B Instruct Apr 2024.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
